117,438 research outputs found
An Analysis Framework for Mobile Workforce Automation
In this paper we introduce an analysis framework for mobile workforce automation. The framework is based on the findings from earlier research as well as on an analysis of 27 recent case studies conducted within the field of mobile workforce automation. It consists of a general reference process for mobile work and of a model explaining influencing factors (worker, task, coordination system, information
system), optimization goals and their relationships in mobile business processes. The framework can be applied to process modeling, simulation, and optimization as well as to requirements analysis and return on investment calculations. Based on the results of case study evaluation, it is furthermore shown, that recent mobile IT solutions are mainly built for relatively simple processes and cooperation models. Mobilizing more complex processes still seems to be a challenge
Analyzing covariate clustering effects in healthcare cost subgroups: insights and applications for prediction
Healthcare cost prediction is a challenging task due to the
high-dimensionality and high correlation among covariates. Additionally, the
skewed, heavy-tailed, and often multi-modal nature of cost data can complicate
matters further due to unobserved heterogeneity. In this study, we propose a
novel framework for finite mixture regression models that incorporates
covariate clustering methods to better account for the effects of clustered
covariates on subgroups of the outcome, which enables a more accurate
characterization of the complex distribution of the data. The proposed
framework can be formulated as a convex optimization problem with an additional
penalty term based on the prior similarity of the covariates. To efficiently
solve this optimization problem, a specialized EM-ADMM algorithm is proposed
that integrates the alternating direction multiplicative method (ADMM) into the
iterative process of the expectation-maximizing (EM) algorithm. The convergence
of the algorithm and the efficiency of the covariate clustering method are
verified using simulation data, and the superiority of the approach over
traditional regression techniques is demonstrated using two real Chinese
medical expenditure datasets. Our empirical results provide valuable insights
into the complex network graph of the covariates and can inform business
practices, such as the design and pricing of medical insurance products.Comment: 36 pages; 7 figure
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
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Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
The role of learning on industrial simulation design and analysis
The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging
from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and
operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond
being a static problem-solving exercise and requires integration with learning. This article discusses the role
of learning in simulation design and analysis motivated by the needs of industrial problems and describes
how selected tools of statistical learning can be utilized for this purpose
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